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A recursive descent evaluation algorithm on policy context similarity

机译:基于策略上下文相似度的递归下降评估算法

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This paper proposes a new policy text similarity calculation method after the analysis and research of the domestic and foreign traditional text similarity calculation based on vector space model. The method which is based on the policy text pretreatment and recursive descent technology presents a computation method of fitting degree among policy clauses and similarity among policy text. The general classic algorithms are involved in the problem of word frequency. This method doesn't establish word frequency vector directly, but it merges the same words. Then by using the recursive descent method, the consistency problem in policy chapters are analyzed from three different levels of policy clauses, policy paragraphs and policy chapters, reducing the space complexity of the fitting calculation. Experimental results show that compared with the existing typical similarity calculation method, this method can increase the efficiency and precision of consistency verification.
机译:在分析和研究基于向量空间模型的国内外传统文本相似度计算方法的基础上,提出了一种新的策略文本相似度计算方法。基于策略文本预处理和递归下降技术的方法提出了一种策略条款之间的契合度和策略文本之间的相似度的计算方法。通用的经典算法都涉及词频问题。该方法不直接建立词频向量,而是合并相同的词。然后采用递归下降法,从政策条款,政策段落和政策章节三个不同层次分析政策章节中的一致性问题,从而降低了拟合计算的空间复杂度。实验结果表明,与现有的典型相似度计算方法相比,该方法可以提高一致性验证的效率和准确性。

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